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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/jetpack.html">Torch-TensorRT in JetPack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/quick_start.html">Quick Start</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/capture_and_replay.html">Introduction</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
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<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/compile_hf_models.html">Compiling LLM models from Huggingface</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Legacy notebooks</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/dynamo.html">torch_tensorrt.dynamo</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="system_overview.html">System Overview</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Writing Dynamo Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
<li class="toctree-l1"><a class="reference internal" href="ts_converters.html">Writing TorchScript Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
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  <section id="writing-dynamo-converters">
<span id="dynamo-converters"></span><h1>Writing Dynamo Converters<a class="headerlink" href="#writing-dynamo-converters" title="Permalink to this heading">¶</a></h1>
<p>The dynamo converter library in Torch-TensorRT is located in <code class="docutils literal notranslate"><span class="pre">TensorRT/py/torch_tensorrt/dynamo/conversion</span></code>.</p>
<section id="converter-implementation">
<h2>Converter implementation<a class="headerlink" href="#converter-implementation" title="Permalink to this heading">¶</a></h2>
<section id="registration">
<h3>Registration<a class="headerlink" href="#registration" title="Permalink to this heading">¶</a></h3>
<p>A converter is a function decrorated with  <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.dynamo_tensorrt_converter</span></code> that follows the function signature:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">conversion</span><span class="o">.</span><span class="n">dynamo_tensorrt_converter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">leaky_relu</span><span class="o">.</span><span class="n">default</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">leaky_relu_converter</span><span class="p">(</span>
    <span class="n">ctx</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">conversion</span><span class="o">.</span><span class="n">ConversionCtx</span><span class="p">,</span>
    <span class="n">target</span><span class="p">:</span> <span class="n">Target</span><span class="p">,</span>
    <span class="n">args</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
    <span class="n">kwargs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Argument</span><span class="p">],</span>
    <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">]]:</span>
</pre></div>
</div>
<p>The decorator takes a number of arguments:</p>
<blockquote>
<div><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">key</span></code>: Node target for which the converter is implemented for (for example, torch.ops.aten.leaky_relu.default)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">enabled</span></code>: Whether the converter should be enabled as a converter that can be used in the converter registry</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">capability_validator</span></code>: A lambda that can take a <code class="docutils literal notranslate"><span class="pre">torch.fx.Node</span></code> and determine if the converter can properly handle this Node. If the validator returns <code class="docutils literal notranslate"><span class="pre">False</span></code>, the subgraph partitioner will make sure this Node is run in PyTorch in the compiled graph.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">priority</span></code>: Allows developers to override existing converters in the converter registry</p></li>
</ul>
</div></blockquote>
<p>All that is required for a converter is the key.</p>
<p>The function body is responsible for taking the current state of the network and adding the next subgraph to perform the op specified in the decorator with TensorRT operations.
The function is provided arguments as the native PyTorch op would be provided with the added case of numpy arrays for frozen Tensor attributes or TensorRT ITensors which are output Tensors of previous nodes, corresponding to edges/output Tensors of intermediate operations in the graph.
To determine the types expected as well as the return type of the converter, look at the definition of the op being converted. In the case of <code class="docutils literal notranslate"><span class="pre">aten</span></code> operations, this file will be the source of truth: <a class="reference external" href="https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml">https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml</a>
Since many converters a developer may write are a composition of lower level operators, instead of needing to implement the converter in raw TensorRT, the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.conversion.impl</span></code> subpackage contains many implementations of operations that can be chained to create a TensorRT subgraph.</p>
<blockquote>
<div><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">ctx</span></code> : The current state of the compiler. Converters primarily will manipulate ctx.net which is the <code class="docutils literal notranslate"><span class="pre">tensorrt.INetworkDefinition</span></code> being constructed. Additional metadata including user provided settings is available in this struct as well.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">target</span></code>: Target key in the <code class="docutils literal notranslate"><span class="pre">call_module</span></code> or <code class="docutils literal notranslate"><span class="pre">call_function</span></code> above. eg: <code class="docutils literal notranslate"><span class="pre">torch.ops.aten_.leaky_relu.default</span></code>. Note that <code class="docutils literal notranslate"><span class="pre">torch.ops.aten._leaky_relu</span></code> is the <code class="docutils literal notranslate"><span class="pre">OpOverloadPacket</span></code> while <code class="docutils literal notranslate"><span class="pre">torch.ops.aten_.leaky_relu.default</span></code> is <code class="docutils literal notranslate"><span class="pre">OpOverload</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">args</span></code>: The arguments being passed to a particular Node (as collected by the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.conversion.TRTInterpreter</span></code>). These arguments along with the kwargs are to be used to construct a specific TensorRT subgraph representing the current node in the INetworkDefinition.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">kwargs</span></code>: The arguments being passed to a particular Node (as collected by the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.conversion.TRTInterpreter</span></code>).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">name</span></code>: String containing the name of the target</p></li>
</ul>
</div></blockquote>
<p>The function is expected to return the <code class="docutils literal notranslate"><span class="pre">tensorrt.ITensor</span></code> or some collection of <code class="docutils literal notranslate"><span class="pre">tensorrt.ITensor</span></code> for use in the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.conversion.TRTInterpreter</span></code> matching the output signature of the operation being converted</p>
</section>
<section id="capability-validation">
<h3>Capability Validation<a class="headerlink" href="#capability-validation" title="Permalink to this heading">¶</a></h3>
<p>There are some converters which have special cases to be accounted for. In those cases, one should use <code class="docutils literal notranslate"><span class="pre">capability_validators</span></code> to register the converter using <code class="docutils literal notranslate"><span class="pre">&#64;dynamo_tensorrt_converter</span></code>
We illustrate this through <code class="docutils literal notranslate"><span class="pre">torch.ops.aten.embedding.default</span></code>. It has parameters - <code class="docutils literal notranslate"><span class="pre">scale_grad_by_freq</span></code> and <code class="docutils literal notranslate"><span class="pre">sparse</span></code> which are not currently supported by the implementation.
In such cases we can write validator <code class="docutils literal notranslate"><span class="pre">embedding_param_validator</span></code> which implements that given those parameters the converter is not supported and register the converter by</p>
</section>
<section id="type-contract">
<h3>Type Contract<a class="headerlink" href="#type-contract" title="Permalink to this heading">¶</a></h3>
<p>The function is expected to follow the type contract established by the signature. This includes accepting the union of valid PyTorch types + numpy arrays for constant tensors and TensorRT ITensors.
In the case that only a subset of types is supported in the converter, you can also add the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.conversion.converter_utils.enforce_tensor_types</span></code>, which allows you to specify a dictionary mapping between input positions and types that those inputs can take. Where possible the decorator will convert inputs to match these types preferring the order provided.
<code class="docutils literal notranslate"><span class="pre">int</span></code> keys in the dictionary will refer to positional arguments in <code class="docutils literal notranslate"><span class="pre">args</span></code>. <code class="docutils literal notranslate"><span class="pre">str</span></code> keys will refer to keyword arguments in <code class="docutils literal notranslate"><span class="pre">kwargs</span></code>.</p>
</section>
<section id="example-convolution">
<h3>Example: <code class="docutils literal notranslate"><span class="pre">Convolution</span></code><a class="headerlink" href="#example-convolution" title="Permalink to this heading">¶</a></h3>
<p>The default convolution converter both uses a capability validator and type enforcement to prevent being run in unsupported situations.
The capability validator is run during partitioning to determine if a particular convolution node can be converted to TensorRT or needs to run in PyTorch. Here the validator ensures that the convolution is no greater than 3D.
The type enforcer will autocast before the converter is called, inputs to the supported type in the converter, thereby limiting the number of cases an author must handle.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@dynamo_tensorrt_converter</span><span class="p">(</span>
    <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">convolution</span><span class="o">.</span><span class="n">default</span><span class="p">,</span> <span class="n">capability_validator</span><span class="o">=</span><span class="k">lambda</span> <span class="n">conv_node</span><span class="p">:</span> <span class="n">conv_node</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span> <span class="ow">in</span> <span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
<span class="nd">@enforce_tensor_types</span><span class="p">(</span>
    <span class="p">{</span>
        <span class="mi">0</span><span class="p">:</span> <span class="p">(</span><span class="n">TRTTensor</span><span class="p">,),</span>
        <span class="mi">1</span><span class="p">:</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">TRTTensor</span><span class="p">),</span>
        <span class="mi">2</span><span class="p">:</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">TRTTensor</span><span class="p">),</span>
    <span class="p">}</span>
<span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aten_ops_convolution</span><span class="p">(</span>
    <span class="n">ctx</span><span class="p">:</span> <span class="n">ConversionContext</span><span class="p">,</span>
    <span class="n">target</span><span class="p">:</span> <span class="n">Target</span><span class="p">,</span>
    <span class="n">args</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
    <span class="n">kwargs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Argument</span><span class="p">],</span>
    <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">TRTTensor</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">TRTTensor</span><span class="p">]]:</span>
</pre></div>
</div>
</section>
</section>
<section id="evaluators">
<h2>Evaluators<a class="headerlink" href="#evaluators" title="Permalink to this heading">¶</a></h2>
<p>Some operations do not produce TensorRT subgraphs as a side-effect. These are termed evaluators.</p>
<blockquote>
<div><p>Example: <code class="docutils literal notranslate"><span class="pre">operator.getitem</span></code></p>
<p>Evaluators are categorized as so since they do not make any modification to the graph. This is implemented in <code class="docutils literal notranslate"><span class="pre">py/torch_tensorrt/dynamo/conversion/op_evaluators.py</span></code>, with the corresponding <code class="docutils literal notranslate"><span class="pre">capbility_validator</span></code>.
The opcode is <code class="docutils literal notranslate"><span class="pre">operator.getitem</span></code>.</p>
</div></blockquote>
</section>
<section id="operator-decomposition">
<h2>Operator Decomposition<a class="headerlink" href="#operator-decomposition" title="Permalink to this heading">¶</a></h2>
<p>There are some converters which can be decomposed into suboperations in PyTorch and need not have separate converter registration.
Such converters can be implemented via a decomposition</p>
<section id="example-addmm">
<h3>Example: <code class="docutils literal notranslate"><span class="pre">addmm</span></code><a class="headerlink" href="#example-addmm" title="Permalink to this heading">¶</a></h3>
<p>The decompositions are registered via <code class="docutils literal notranslate"><span class="pre">register_torch_trt_decomposition</span></code> decorator
We define <code class="docutils literal notranslate"><span class="pre">addmm_replacement</span></code> and replace it with the torch ops, which will have their corresponding converters called.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">lowering</span><span class="o">.</span><span class="n">register_torch_trt_decomposition</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">addmm</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">addmm_replacement</span><span class="p">(</span>
    <span class="n">input_</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">mat1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">mat2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">input_</span><span class="p">,</span> <span class="n">beta</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">mat1</span><span class="p">,</span> <span class="n">mat2</span><span class="p">),</span> <span class="n">alpha</span><span class="p">)</span>
    <span class="p">)</span>
</pre></div>
</div>
<p>You can modify the decompositions run by editing <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.lowering.torch_enabled_decompositions</span></code> and <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.lowering.torch_disabled_decompositions</span></code></p>
<blockquote>
<div><p>Note: <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.lowering.torch_enabled_decompositions</span></code> and <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.lowering.torch_disabled_decompositions</span></code> must be disjoint sets and that the decompositions already defined in <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.lowering</span></code> will take precedence over torch lowering ops.</p>
</div></blockquote>
<p>Much of the time, this is significantly easier than implementing a converter. So where possible, this is what should be tried first.</p>
</section>
</section>
</section>


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              <ul>
<li><a class="reference internal" href="#">Writing Dynamo Converters</a><ul>
<li><a class="reference internal" href="#converter-implementation">Converter implementation</a><ul>
<li><a class="reference internal" href="#registration">Registration</a></li>
<li><a class="reference internal" href="#capability-validation">Capability Validation</a></li>
<li><a class="reference internal" href="#type-contract">Type Contract</a></li>
<li><a class="reference internal" href="#example-convolution">Example: <code class="docutils literal notranslate"><span class="pre">Convolution</span></code></a></li>
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